I am marching towards Synergetic & Holistic Intelligence.
My long term goal is to advance AI research and technologies in interrelated fields such as computer vision, machine learning, language understanding and robotics, to build intelligent systems, either virtual or embodied, to facilitate understanding multiple sensory inputs, to gain actionable insights from perception to cognition, to solve important real-world problems and to better serve our human race.
In the medium term, I am putting more emphasis on computer vision, machine learning and their applications, with a strong focus on accurate and efficient understanding of various types of objects and activities from sensory inputs such as images and videos. Over the past few years, I have explored a wide range of topics towards accurate and efficient visual understanding: from image-level classification, to instance-level object detection, to video-level detection and tracking, and more recently to spatio-temporal activity recognition and pixel-level segmentation etc. My team and I have been lucky to have won some international AI competitions and set new state-of-the-arts on major computer vision benchmarks. I am also fortunate to have been working on a broad spectrum of applied research projects with more than $10 million support, from research assistant, to team leader, and PI/Co-PI, with collaborators and support from industry, academic units and government agencies. This enables me to understand the true depth of challenges arose from real-world data and problems, or even in collaboration, management and technology transfer.
To emphasize, my current research focuses on accurate & efficient visual understanding for AI systems & application, in particular I have recently worked in:
- Computer Vision: classification, object detection, segmentation, activity recognition, etc.
- Machine Learning: deep learning, weakly-supervised learning, transfer learning, efficient learning, etc.
- AI Systems & Applications for Science, Education, Agriculture, Medcine, Finance, Transportation, etc.
My research activities include multiple aspects to solve such problems and to advance AI research: projects, papers, competitions, organizing workshops, training students etc.
Please find more publication and technical reports on Google Scholar.
Abbreviations: [C]: Conference; [J] Journal; [W] Workshop; [TR]: Technical Report; [B]: Book; [P]: Patent; [Comp]: Competition; [Proj]: Project; [Org] Program Organization; [SOTA]: State-of-the-art (at the time of publication)
Classification & Learning:
- [TR] Any-Precision Deep Neural Networks, Haichao Yu, Haoxiang Li, Honghui Shi, Thomas S. Huang, Gang Hua, ArXiv Preprint, 2019
- [TR] Revisiting Pre-training: An Efficient Training Method for Image Classification, Bowen Cheng, Yunchao Wei, Honghui Shi, Shiyu Chang, Jinjun Xiong, Thomas S. Huang, ArXiv Preprint, 2018
- [C] SpotTune: Transfer Learning through Adaptive Fine-tuning, Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, Rogerio Feris, CVPR, 2019 (SOTA on Visual Decathlon Challenge, acceptance rate 25.2 %)
- [C] Galaxy Classification Using Deep Convolutional Neural Networks, Honghui Shi, Thomas Huang, GTC, 2015
- [Comp] Galaxy Zoo - The Galaxy Challenge on Kaggle, Silver Medal (2014)
- [TR] Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection, Bowen Cheng, Yunchao Wei, Honghui Shi, Rogerio Feris, Jinjun Xiong, Thomas Huang, ArXiv Preprint, 2018
- [C] Learning Object Detection from Scratch via Gated Feature Reuse , Zhiqiang Shen, Honghui Shi, Jiahui Yu, Hai Phan, Rogerio Feris, Liangliang Cao, Ding Liu, Xinchao Wang, Thomas S. Huang, Marios Savvides, BMVC, 2019
- [C] Revisiting RCNN: On Awakening the Classification Power of Faster RCNN, Bowen Cheng, Yunchao Wei, Honghui Shi, Rogerio Feris, Jinjun Xiong, Thomas Huang, ECCV, 2018 (SOTA on PASCAL VOC, COCO, acceptance rate 31.8 %)
- [C] TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection, Yunchao Wei, Zhiqiang Shen, Bowen Cheng, Honghui Shi, Jinjun Xiong, Jiashi Feng, Thomas Huang, ECCV, 2018 (acceptance rate 31.8 %)
- [TR] Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids, Zhiqiang Shen, Honghui Shi, Rogerio Feris, Liangliang Cao, Shuicheng Yan, Ding Liu, Xinchao Wang, Xiangyang Xue, Thomas S. Huang, ArXiv Preprint, 2017
- [C] Effective Object Detection from Traffic Camera Videos, Honghui Shi, Zhichao Liu, Yuchen Fan, Xinchao Wang, Thomas Huang, IEEE Smart World Congress, 2017 (Invited paper)
- [Comp] Nvidia AI City Challenge 1st Place (2017)
- [W] Improving Context Modeling for Video Object Detection and Tracking, Yunchao Wei, Mengdan Zhang, Jianan Li, Yunpeng Chen, Jiashi Feng, Honghui Shi, Jian Dong, Shuicheng Yan, Beyond ImageNet Large Scale Visual Recognition Challenge @ CVPR, 2017
- [Comp] ImageNet Video Object Detection and Tracking Challenge 2nd Place (2017)
- [TR] Seq-NMS for Video Object Detection, Wei Han, Pooya Khorrami, Tom Le Paine, Prajit Ramachandran, Mohammad Babaeizadeh, Honghui Shi, Jianan Li, Shuicheng Yan, Thomas S. Huang, ArXiv Preprint, 2016
- [Comp] ImageNet Video Object Detection Challenge 3rd Place (2015)
- [TR] AlignSeg: Feature-Aligned Segmentation Networks, Zilong Huang, Yunchao Wei, Xinggang Wang, Honghui Shi, Wenyu Liu, Thomas Huang, ArXiv Preprint, 2020 (SOTA on semantic segmentation)
- [C] Differential Treatment for Stuff and Things: A Simple Unsupervised DomainAdaptation Method for Semantic Segmentation, Zhonghao Wang, Mo Yu, Yunchao Wei, Rogerio Feris, Jinjun Xiong, Wen-mei Hwu, Thomas Huang, Honghui Shi, CVPR, 2020 (SOTA on unsupervised domain adaptation for semantic segmentation, acceptance rate 22.0 %)
- [Proj] A Large-scale Dataset for Text Segmentation, Sponsored by Adobe Research
- [C] SPGNet: Semantic Prediction Guidance for Scene Parsing, Bowen Cheng, Liang-Chieh Chen, Yunchao Wei, Yukun Zhu, Zilong Huang, Jinjun Xiong, Thomas Huang, Wen-mei Hwu, Honghui Shi, ICCV, 2019 (SOTA on Cityscapes, acceptance rate 25.0 %)
- [C] Geometry-Aware Distillation for Indoor Semantic Segmentation, Jianbo Jiao, Yunchao Wei, Zequn Jie, Honghui Shi, Rynson W.H. Lau, Thomas S. Huang, CVPR, 2019 (acceptance rate 25.2 %)
- [C] Weakly Supervised Scene Parsing with Point-based Distance Metric Learning, Rui Qian, Yunchao Wei, Honghui Shi, Jiachen Li, Jiaying Liu, Thomas Huang, AAAI, 2019 (acceptance rate 16.2 %)
- [C] Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation, Yunchao Wei, Huaxin Xiao, Honghui Shi, Zequn Jie, Jiashi Feng, Thomas S. Huang, CVPR, 2018 (Spotlight Oral, acceptance rate 6.7 %)
- [Proj] Deep Intermodal Video Analytics (DIVA), sponsored by IARPA, 2017.10 - 2021.09
- [W] Object-Centric Spatio-Temporal Activity Detection and Recognition, Mandis Beigi, Lisa M Brown, Quanfu Fan, John Henning, Chung-Ching Lin, Honghui Shi, Chiao-fe Shu, Rogerio Feris, NIST TRECVID Workshop, 2018
- [Comp] NIST/IARPA TRECVID Activity Recognition Challenge 1st Place (2018)
Visual Relationship, Reasoning, Grounding; Vision + Language:
- [Proj] Collaborative research on multimedia with Blender Lab at UIUC
- [Proj] Collaborative research on visual reasoning with IBM Research
- [Comp] Visual Relationship Detection - Google AI Open Images Challenge, Silver Medal (2018)
Human-Centered Vision Tasks:
- HigherHRNet: Scare-Aware Representation Learning for Bottom-Up Human Pose Estimation, Bowen Cheng, Bin Xiao, Jingdong Wang, Honghui Shi, Thomas Huang, Lei Zhang, CVPR, 2020 (SOTA on human pose estimation, acceptance rate 22.0 %)
- Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-ID, Yang Fu, Yunchao Wei, Guanshuo Wang, Yuqian Zhou, Honghui Shi, Thomas Huang, ICCV, 2019 (SOTA on cross dataset re-id, Oral, acceptance rate 4.3 %)
- Horizontal Pyramid Matching for Person Re-ID. Yang Fu, Yunchao Wei, Yuqian Zhou, Honghui Shi, Gao Huang, Xinchao Wang, Zhiqiang Yao, Thomas Huang, AAAI, 2019 (SOTA on person re-id, acceptance rate 16.2 %)
- [TR] SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems, Xiaofan Zhang, Haoming Lu, Cong Hao, Jiachen Li, Bowen Cheng, Yuhong Li, Kyle Rupnow, Jinjun Xiong, Thomas Huang, Honghui Shi, Wen-mei Hwu, Deming Chen, MLSys, 2020 (Oral and Poster, acceptance rate 20.0 %)
- [TR] SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection, Xiaofan Zhang, Cong Hao, Haoming Lu, Jiachen Li, Yuhong Li, Yuchen Fan, Kyle Rupnow, Jinjun Xiong, Thomas Huang, Honghui Shi, Wen-mei Hwu, Deming Chen, DAC System Design Contest Technical Report, 2019
- [Comp] IEEE/ACM DAC System Design Contest 1st Place (2019)
AI Applications - Science & Engineering:
- [Proj] Cement Phase Segmentation, collaborated with UIUC Civil Engineering
- [Proj] Galaxy Classification, collaborated with UIUC Astronomy
- [Proj] Gravitational Lens Detection, collaborated with UIUC Astronomy
AI Applications - Education:
- [Proj] Intelligent Learning Advisor, sponsored by IBM Research
- [Proj] AI for Education, sponsored by New Oriental Education Technology
AI Applications - Agriculture:
- [Org] The 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture, CVPR 2020
- [C] Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis, Mang Tik Chiu*, Xingqian Xu*, Yunchao Wei, Zilong Huang, Alexander Schwing, Robert Brunner, Hrant Khachatrian, Hovnatan Karapetyan, Ivan Dozier, Greg Rose, David Wilson, Adrian Tudor, Naira Hovakimyan, Thomas S. Huang, Honghui Shi, CVPR, 2020 (a novel dataset for agriculture, acceptance rate 22.0 %)
- [Proj] Deep Pattern Analysis in Agricultural Images, sponsored by IntelinAir
AI Applications - Medicine:
- [C] FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation, Hanchao Yu, Shanhui Sun, Haichao Yu, Xiao Chen, Honghui Shi, Thomas Huang, Terrence Chen, CVPR, 2020 (fast & online adaptation method, acceptance rate 22.0 %)
- [Proj] Multi-modal Medical Image Understanding, sponsored by Jump ARCHES
- [Proj] Multiphoton Image Analysis for Cancer Diagnosis, sponsored by Mayo Clinic & UIUC
AI Applications - Finance:
- [Proj] Deep Learning in Financial Modeling and Strategy, sponsored by Jump Trading